面向探地雷達(dá) B-scan圖像的目標(biāo)檢測(cè)算法綜述

侯斐斐; 施榮華; 雷文太; 董健; 許孟迪; 席景春

doi:10.11999/JEIT190680

面向探地雷達(dá) B-scan圖像的目標(biāo)檢測(cè)算法綜述

doi: 10.11999/JEIT190680 cstr: 32379.14.JEIT190680

中南大學(xué)計(jì)算機(jī)學(xué)院長(zhǎng)沙 410000

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61102139, 61872390)，中南大學(xué)基礎(chǔ)研究基金(2018zzts181)

詳細(xì)信息

作者簡(jiǎn)介:
侯斐斐：女，1993年生，博士生，研究方向?yàn)樘降乩走_(dá)，深度學(xué)習(xí)，圖像處理

施榮華：男，1963年生，教授，博士生導(dǎo)師，研究方向?yàn)樯漕l系統(tǒng)集成和量子技術(shù)

雷文太：男，1979年生，副教授，博士生導(dǎo)師，研究方向?yàn)樘降乩走_(dá)系統(tǒng)集成和信號(hào)處理

通訊作者:
雷文太　leiwentai@csu.edu.cn

中圖分類(lèi)號(hào): TN957.51
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出版歷程
- 收稿日期: 2019-09-04
- 修回日期: 2019-11-12
- 網(wǎng)絡(luò)出版日期: 2019-11-18
- 刊出日期: 2020-01-21

A Review of Target Detection Algorithm for GPR B-scan Processing

Department of Computer Science and Engineering, Central South University, Changsha 410000, China

Funds: The National Natural Science Foundation of China (61102139, 61872390), The Fundamental Rresearch Funds for the Central Universities of Central South University (2018zzts181)

摘要

摘要:
利用無(wú)損探測(cè)技術(shù)來(lái)獲取地下目標(biāo)的信息是當(dāng)前研究的熱點(diǎn)，探地雷達(dá)(GPR)作為一種重要的無(wú)損工具，已被廣泛用于檢測(cè)，定位和特征化地下目標(biāo)。然而，從GPR成像中探測(cè)掩埋物體并評(píng)估其位置既費(fèi)時(shí)又費(fèi)力。因此，實(shí)現(xiàn)地下目標(biāo)的自動(dòng)化探測(cè)對(duì)實(shí)際應(yīng)用是必要的。為此，該文在綜合分析地下目標(biāo)回波特征的基礎(chǔ)上，討論了使用GPR評(píng)估目標(biāo)位置的可行性，并回顧了國(guó)內(nèi)外學(xué)者在GPR成像中對(duì)雙曲線特征自動(dòng)化檢測(cè)的研究進(jìn)展。該文還在國(guó)內(nèi)外典型實(shí)例剖析的基礎(chǔ)上，總結(jié)并比較了目標(biāo)檢測(cè)的處理方法。最后指出，未來(lái)的研究應(yīng)集中于開(kāi)發(fā)新的深度學(xué)習(xí)檢測(cè)框架，用以自動(dòng)檢測(cè)和估計(jì)真實(shí)場(chǎng)景中的地下特征。
- 探地雷達(dá) /
- 地下目標(biāo)檢測(cè) /
- 機(jī)器學(xué)習(xí) /
- 深度學(xué)習(xí) /
- 雙曲線反射
Abstract:
Ground Penetrating Radar (GPR), as a non-destructive technology, has been widely used to detect, locate, and characterize subsurface objects. Example applications include underground utility mapping and bridge deck deterioration assessment. However, manually interpreting the GPR scans to detect buried objects and estimate their positions is time-consuming and labor-intensive. Hence, the automatic detection of targets is necessary for practical application. To this end, this paper discusses the feasibility of using GPR to estimate target positions, and reviews the progress made by domestic and international scholars on automatic hyperbolic signature detection in GPR scans. Thereafter, this paper summarizes and compares the processing methods for target detection. It is concluded that future research should focus on developing deep-learning based method to automatically detect and estimate subsurface features for on-site applications.
- Ground Penetrating Radar (GPR) /
- Underground target detection /
- Machine Learning (ML) /
- Deep Learning (DL) /
- Hyperbolic reflection

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圖 1 真實(shí)場(chǎng)景中GPR B-scan中的雙曲線特征

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圖 2 在文獻(xiàn)[28]中解決的一些復(fù)雜情況示例

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圖 3 DCSE和OSCA算法的對(duì)比結(jié)果展示

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圖 4 基于CTFP算法的雙曲線擬合結(jié)果展示

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表 1 GPR目標(biāo)檢測(cè)的經(jīng)典算法總結(jié)

序號(hào)	參考文獻(xiàn)	時(shí)間	GPR目標(biāo)	客觀評(píng)價(jià)
1	Borgioli et al.^[17]	2008	地埋管道	在Hough變換中引入加權(quán)因子，解決了管道靠近時(shí)雙曲線重疊的問(wèn)題；但是需要預(yù)備模型，計(jì)算成本相對(duì)較高。
2	Maas et al. ^[23]	2013	雙曲線反射	使用Viola-Jones算法標(biāo)記目標(biāo)候選區(qū)域，它避免了模板匹配并縮小了后續(xù)搜索區(qū)域；然而，應(yīng)用特征需手動(dòng)識(shí)別，分類(lèi)結(jié)果取決于特征的質(zhì)量，難度隨著數(shù)據(jù)量的增加。
3	Besaw et al.^[2]	2016	地埋爆炸物	應(yīng)用CNN從GPR B-scan中提取有意義的特征并對(duì)目標(biāo)進(jìn)行分類(lèi)。交叉驗(yàn)證，網(wǎng)絡(luò)權(quán)重正則化和“dropout”用于防止過(guò)度訓(xùn)練。
4	Besaw^[3]	2016	地埋爆炸物	在CNN基礎(chǔ)上增加了額外的Data Augmentation技術(shù)，用于增加可用訓(xùn)練數(shù)據(jù)的數(shù)量和可變性。
5	文獻(xiàn)[4,5]	2017	地埋爆炸物	研究了預(yù)訓(xùn)練CNN的初始化步驟，以解決GPR數(shù)據(jù)標(biāo)記樣本不足的問(wèn)題；但是輸入網(wǎng)絡(luò)中真實(shí)圖像的大小和數(shù)量通常是有限的，僅實(shí)現(xiàn)分類(lèi)步驟。
6	Pham et al. ^[27]	2018	雙曲線反射	首次采用Faster RCNN來(lái)檢測(cè)GPR B-scan中的反射雙曲線。該技術(shù)在真實(shí)測(cè)試集上的性能要超過(guò)使用HOG或Haar-like特征的檢測(cè)器，但缺少定量的評(píng)估。
7	Lei et al. ^[28]	2019	地埋鋼筋	在文獻(xiàn)[27]基礎(chǔ)上，采用了DA手段增加真實(shí)GPR數(shù)據(jù)集和仿真數(shù)據(jù)集；提出DCSE算法以識(shí)別雙曲線特征，完善了文獻(xiàn)[30]中提出的OSCA算法；提出CTFP算法自動(dòng)提取擬合點(diǎn)。所提出方案的有效性在仿真和真實(shí)數(shù)據(jù)集上得到了驗(yàn)證。
8	Dou et al. ^[29]	2016	雙曲線反射	提出了C3算法分割交叉雙曲線，并將其送入神經(jīng)網(wǎng)絡(luò)進(jìn)行分類(lèi)。C3算法水平掃描B-scan圖像中的每個(gè)像素以進(jìn)行聚類(lèi)。然而，雙曲線是垂直向下打開(kāi)的，C3算法沒(méi)有考慮這個(gè)重要特征。
9	Zhou et al. ^[30]	2018	金屬管道水泥管道	提出OSCA算法解決了文獻(xiàn)[29]中的難題，可以識(shí)別具有向下開(kāi)口特征的聚類(lèi)。然而，在整個(gè)圖像上進(jìn)行OSCA算法是不合適的，因?yàn)殡y以處理包含太多非平穩(wěn)噪聲的大型現(xiàn)場(chǎng)數(shù)據(jù)集，導(dǎo)致后續(xù)處理復(fù)雜化。

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